Techniques and apparatus for coarse granularity scalable lifting for point-cloud attribute coding

ABSTRACT

A method, computer system, and computer-readable medium are provided for point cloud attribute coding by at least one processor. Data associated with a point cloud is received. The received data is transformed through a lifting decomposition based on enabling a scalable coding of attributes associated with the lifting decomposition. The point cloud is reconstructed based on the transformed data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional PatentApplication Nos. 62/958,863 (filed Jan. 9, 2020); 63/009,874 (filed Apr.14, 2020); and 63/009,875 (filed Apr. 14, 2020) filed in the U.S. Patentand Trademark Office, which are incorporated herein by reference intheir entirety.

BACKGROUND 1. Field

Methods and apparatuses consistent with embodiments relate tograph-based point cloud compression (G-PCC), and more particularly, amethod and an apparatus for coding of attribute information of pointcloud samples.

2. Background

Advanced three-dimensional (3D) representations of the world areenabling more immersive forms of interaction and communication, and alsoallow machines to understand, interpret and navigate our world. 3D pointclouds have emerged as an enabling representation of such information. Anumber of use cases associated with point cloud data have beenidentified, and corresponding requirements for point cloudrepresentation and compression have been developed.

A point cloud is a set of points in a 3D space, each with associatedattributes, e.g., color, material properties, etc. Point clouds can beused to reconstruct an object or a scene as a composition of suchpoints. They can be captured using multiple cameras and depth sensors invarious setups, and may be made up of thousands up to billions of pointsto realistically represent reconstructed scenes.

Compression technologies are needed to reduce the amount of data torepresent a point cloud. As such, technologies are needed for lossycompression of point clouds for use in real-time communications and sixdegrees of freedom (6DoF) virtual reality. In addition, technology issought for lossless point cloud compression in the context of dynamicmapping for autonomous driving and cultural heritage applications, etc.The Moving Picture Experts Group (MPEG) has started working on astandard to address compression of geometry and attributes such ascolors and reflectance, scalable/progressive coding, coding of sequencesof point clouds captured over time, and random access to subsets of apoint cloud.

FIG. 1A is a diagram illustrating a method of generating levels ofdetail (LoD) in G-PCC.

Referring to FIG. 1A, in current G-PCC attributes coding, an LoD (i.e.,a group) of each 3D point (e.g., P0-P9) is generated based on a distanceof each 3D point, and then attribute values of 3D points in each LoD isencoded by applying prediction in an LoD-based order 110 instead of anoriginal order 105 of the 3D points. For example, an attributes value ofthe 3D point P2 is predicted by calculating a distance-based weightedaverage value of the 3D points P0, P5 and P4 that were encoded ordecoded prior to the 3D point P2.

An example anchor method in G-PCC proceeds as follows.

First, a variability of a neighborhood of a 3D point is computed tocheck how different neighbor values are, and if the variability is lowerthan a threshold, the calculation of the distance-based weighted averageprediction is conducted by predicting attribute values(a_(i))_(i∈0 . . . k−1), using a linear interpolation process based ondistances of nearest neighbors of a current point i. Let

_(i) be a set of k-nearest neighbors of the current point i, let

$\left( {\overset{\sim}{a}}_{j} \right)_{j \in \mathcal{N}_{i}}$be their decoded/reconstructed attribute values and let

(δ_(j))_(j ∈ 𝒩_(i))be their distances to the current point i. A predicted attribute valueâ_(i) is then given by:

$\begin{matrix}{{\hat{a}}_{i} = {{{Round}\left( {\frac{1}{2}{\sum\limits_{j \in \mathcal{N}_{i}}{\frac{\frac{1}{\delta_{j}^{2}}}{\sum\limits_{j \in \mathcal{N}_{i}}\frac{1}{\delta_{j}^{2}}}{\overset{\sim}{a}}_{j}}}} \right)}.}} & \left( {{Eq}.1} \right)\end{matrix}$

Note that geometric locations of all point clouds are already availablewhen attributes are coded. In addition, the neighboring points togetherwith their reconstructed attribute values are available both at anencoder and a decoder as a k-dimensional tree structure that is used tofacilitate a nearest neighbor search for each point in an identicalmanner.

Second, if the variability is higher than the threshold, arate-distortion optimized (RDO) predictor selection is performed.Multiple predictor candidates or candidate predicted values are createdbased on a result of a neighbor point search in generating LoD. Forexample, when the attributes value of the 3D point P2 is encoded byusing prediction, a weighted average value of distances from the 3Dpoint P2 to respectively the 3D points P0, P5 and P4 is set to apredictor index equal to 0. Then, a distance from the 3D point P2 to thenearest neighbor point P4 is set to a predictor index equal to 1.Moreover, distances from the 3D point P2 to respectively the nextnearest neighbor points P5 and P0 are set to predictor indices equal to2 and 3, as shown in Table 1 below.

TABLE 1 Sample of predictor candidate for attributes coding Predictorindex Predicted value 0 average 1 P4 (1^(st) nearest point) 2 P5 (2^(nd)nearest point) 3 P0 (3 ^(rd) nearest point)

After creating predictor candidates, a best predictor is selected byapplying a rate-distortion optimization procedure, and then, a selectedpredictor index is mapped to a truncated unary (TU) code, bins of whichwill be arithmetically encoded. Note that a shorter TU code will beassigned to a smaller predictor index in Table 1.

A maximum number of predictor candidates MaxNumCand is defined and isencoded into an attributes header. In the current implementation, themaximum number of predictor candidates MaxNumCand is set to equal tonumberOfNearestNeighborsInPrediction+1 and is used in encoding anddecoding predictor indices with a truncated unary binarization.

A lifting transform for attribute coding in G-PCC builds on top of apredicting transform described above. A main difference between theprediction scheme and the lifting scheme is the introduction of anupdate operator.

FIG. 1B is a diagram of an architecture for P/U(Prediction/Update)-lifting in G-PCC. To facilitate prediction andupdate steps in lifting, one has to split a signal into two sets ofhigh-correlation at each stage of decomposition. In the lifting schemein G-PCC, the splitting is performed by leveraging an LoD structure inwhich such high-correlation is expected among levels and each level isconstructed by a nearest neighbor search to organize non-uniform pointclouds into a structured data. A P/U decomposition step at a level Nresults in a detail signal D(N−1) and an approximation signal A(N−1),which is further decomposed into D(N−2) and A(N−2). This step isrepeatedly applied until a base layer approximation signal A(1) isobtained.

Consequently, instead of coding an input attribute signal itself thatconsists of LOD(N), . . . , LOD(1), one ends up coding D(N−1), D(N−2), .. . , D(1), A(1) in the lifting scheme. Note that application ofefficient P/U steps often leads to sparse sub-bands “coefficients” inD(N−1), . . . , D(1), thereby providing a transform coding gainadvantage.

Currently, a distance-based weighted average prediction described abovefor the predicting transform is used for a prediction step in thelifting as an anchor method in G-PCC.

In prediction and lifting for attribute coding in G-PCC, an availabilityof neighboring attribute samples is important for compression efficiencyas more of the neighboring attribute samples can provide betterprediction. In a case in which there are not enough neighbors to predictfrom, the compression efficiency can be compromised.

SUMMARY

According to embodiments, a method of interframe point cloud attributecoding is performed by at least one processor and includes receivingdata associated with a point cloud is received. The received data istransformed through a lifting decomposition based on enabling a scalablecoding of attributes associated with the lifting decomposition. Thepoint cloud is reconstructed based on the transformed data.

According to embodiments, an apparatus for interframe point cloudattribute coding includes at least one memory configured to storecomputer program code, and at least one processor configured to accessthe at least one memory and operate according to the computer programcode. The computer program code includes receiving data associated witha point cloud is received. The received data is transformed through alifting decomposition based on enabling a scalable coding of attributesassociated with the lifting decomposition. The point cloud isreconstructed based on the transformed data.

A non-transitory computer-readable storage medium storing instructionsthat cause at least one processor to receiving data associated with apoint cloud is received. The received data is transformed through alifting decomposition based on enabling a scalable coding of attributesassociated with the lifting decomposition. The point cloud isreconstructed based on the transformed data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a diagram illustrating a method of generating LoD in G-PCC.

FIG. 1B is a diagram of an architecture for P/U-lifting in G-PCC.

FIG. 2 is a block diagram of a communication system according toembodiments.

FIG. 3 is a diagram of a placement of a G-PCC compressor and a G-PCCdecompressor in an environment, according to embodiments.

FIG. 4 is a functional block diagram of the G-PCC compressor accordingto embodiments.

FIG. 5 is a functional block diagram of the G-PCC decompressor accordingto embodiments.

FIG. 6 is a flowchart illustrating a method of coding of attributeinformation of point cloud samples, according to embodiments.

FIG. 7 is a diagram of a computer system suitable for implementingembodiments.

DETAILED DESCRIPTION

Embodiments described herein provide a method and an apparatus forcoding of attribute information of point cloud samples. The method andthe apparatus of coding of attribute information of point cloud samplescan also work for any codecs with a similar structure.

As previously described, a distance-based weighted average prediction isused for a prediction step in the lifting as an anchor method in G-PCC.In prediction and lifting for attribute coding in G-PCC, an availabilityof neighboring attribute samples is important for compression efficiencyas more of the neighboring attribute samples can provide betterprediction. In a case in which there are not enough neighbors to predictfrom, the compression efficiency can be compromised. However, for pointcloud data, there are scenarios where scalable reconstruction of datafrom lossy to lossless or near-lossless fidelity is required. It may beadvantageous, therefore, to enable the scalable coding of attributesunder the current GPCC lifting design. Thus, the method, computersystem, and computer-readable medium disclosed herein directly pertainsto the current G-PCC lifting design and proposes methods of extending ormodifying it to enable scalable coding of lifting coefficients. Thesemethods can be applied to similar codecs designed for point clouds.

FIG. 2 is a block diagram of a communication system 200 according toembodiments. The communication system 200 may include at least twoterminals 210 and 220 interconnected via a network 250. Forunidirectional transmission of data, a first terminal 210 may code pointcloud data at a local location for transmission to a second terminal 220via the network 250. The second terminal 220 may receive the coded pointcloud data of the first terminal 210 from the network 250, decode thecoded point cloud data and display the decoded point cloud data.Unidirectional data transmission may be common in media servingapplications and the like.

FIG. 2 further illustrates a second pair of terminals 230 and 240provided to support bidirectional transmission of coded point cloud datathat may occur, for example, during videoconferencing. For bidirectionaltransmission of data, each terminal 230 or 240 may code point cloud datacaptured at a local location for transmission to the other terminal viathe network 250. Each terminal 230 or 240 also may receive the codedpoint cloud data transmitted by the other terminal, may decode the codedpoint cloud data and may display the decoded point cloud data at a localdisplay device.

In FIG. 2 , the terminals 210-240 may be illustrated as servers,personal computers and smartphones, but principles of the embodimentsare not so limited. The embodiments find application with laptopcomputers, tablet computers, media players and/or dedicated videoconferencing equipment. The network 250 represents any number ofnetworks that convey coded point cloud data among the terminals 210-240,including for example wireline and/or wireless communication networks.The communication network 250 may exchange data in circuit-switchedand/or packet-switched channels. Representative networks includetelecommunications networks, local area networks, wide area networksand/or the Internet. For the purposes of the present discussion, anarchitecture and topology of the network 250 may be immaterial to anoperation of the embodiments unless explained herein below.

FIG. 3 is a diagram of a placement of a G-PCC compressor 303 and a G-PCCdecompressor 310 in an environment, according to embodiments. Thedisclosed subject matter can be equally applicable to other point cloudenabled applications, including, for example, video conferencing,digital TV, storing of compressed point cloud data on digital mediaincluding CD, DVD, memory stick and the like, and so on.

A streaming system 300 may include a capture subsystem 313 that caninclude a point cloud source 301, for example a digital camera,creating, for example, uncompressed point cloud data 302. The pointcloud data 302 having a higher data volume can be processed by the G-PCCcompressor 303 coupled to the point cloud source 301. The G-PCCcompressor 303 can include hardware, software, or a combination thereofto enable or implement aspects of the disclosed subject matter asdescribed in more detail below. Encoded point cloud data 304 having alower data volume can be stored on a streaming server 305 for futureuse. One or more streaming clients 306 and 308 can access the streamingserver 305 to retrieve copies 307 and 309 of the encoded point clouddata 304. A client 306 can include the G-PCC decompressor 310, whichdecodes an incoming copy 307 of the encoded point cloud data and createsoutgoing point cloud data 311 that can be rendered on a display 312 orother rendering devices (not depicted). In some streaming systems, theencoded point cloud data 304, 307 and 309 can be encoded according tovideo coding/compression standards. Examples of those standards includethose being developed by MPEG for G-PCC.

FIG. 4 is a functional block diagram of a G-PCC compressor 303 accordingto embodiments.

As shown in FIG. 4 , the G-PCC compressor 303 includes a quantizer 405,a points removal module 410, an octree encoder 415, an attributestransfer module 420, an LoD generator 425, a prediction module 430, aquantizer 435 and an arithmetic coder 440.

The quantizer 405 receives positions of points in an input point cloud.The positions may be (x,y,z)-coordinates. The quantizer 405 furtherquantizes the received positions, using, e.g., a scaling algorithmand/or a shifting algorithm.

The points removal module 410 receives the quantized positions from thequantizer 405, and removes or filters duplicate positions from thereceived quantized positions.

The octree encoder 415 receives the filtered positions from the pointsremoval module 410, and encodes the received filtered positions intooccupancy symbols of an octree representing the input point cloud, usingan octree encoding algorithm. A bounding box of the input point cloudcorresponding to the octree may be any 3D shape, e.g., a cube.

The octree encoder 415 further reorders the received filtered positions,based on the encoding of the filtered positions.

The attributes transfer module 420 receives attributes of points in theinput point cloud. The attributes may include, e.g., a color or RGBvalue and/or a reflectance of each point. The attributes transfer module420 further receives the reordered positions from the octree encoder415.

The attributes transfer module 420 further updates the receivedattributes, based on the received reordered positions. For example, theattributes transfer module 420 may perform one or more amongpre-processing algorithms on the received attributes, the pre-processingalgorithms including, for example, weighting and averaging the receivedattributes and interpolation of additional attributes from the receivedattributes. The attributes transfer module 420 further transfers theupdated attributes to the prediction module 430.

The LoD generator 425 receives the reordered positions from the octreeencoder 415, and obtains an LoD of each of the points corresponding tothe received reordered positions. Each LoD may be considered to be agroup of the points, and may be obtained based on a distance of each ofthe points. For example, as shown in FIG. 1A, points P0, P5, P4 and P2may be in an LoD LOD0, points P0, P5, P4, P2, P1, P6 and P3 may be in anLoD LOD1, and points P0, P5, P4, P2, P1, P6, P3, P9, P8 and P7 may be inan LoD LOD2.

The prediction module 430 receives the transferred attributes from theattributes transfer module 420, and receives the obtained LoD of each ofthe points from the LoD generator 425. The prediction module 430 obtainsprediction residuals (values) respectively of the received attributes byapplying a prediction algorithm to the received attributes in an orderbased on the received LoD of each of the points. The predictionalgorithm may include any among various prediction algorithms such as,e.g., interpolation, weighted average calculation, a nearest neighboralgorithm and RDO.

For example, as shown in FIG. 1A, the prediction residuals respectivelyof the received attributes of the points P0, P5, P4 and P2 included inthe LoD LOD0 may be obtained first prior to those of the receivedattributes of the points P1, P6, P3, P9, P8 and P7 included respectivelyin the LoDs LOD1 and LOD2. The prediction residuals of the receivedattributes of the point P2 may be obtained by calculating a distancebased on a weighted average of the points P0, P5 and P4.

The quantizer 435 receives the obtained prediction residuals from theprediction module 430, and quantizes the received predicted residuals,using, e.g., a scaling algorithm and/or a shifting algorithm.

The arithmetic coder 440 receives the occupancy symbols from the octreeencoder 415, and receives the quantized prediction residuals from thequantizer 435. The arithmetic coder 440 performs arithmetic coding onthe received occupancy symbols and quantized predictions residuals toobtain a compressed bitstream. The arithmetic coding may include anyamong various entropy encoding algorithms such as, e.g.,context-adaptive binary arithmetic coding.

FIG. 5 is a functional block diagram of a G-PCC decompressor 310according to embodiments.

As shown in FIG. 5 , the G-PCC decompressor 310 includes an arithmeticdecoder 505, an octree decoder 510, an inverse quantizer 515, an LoDgenerator 520, an inverse quantizer 525 and an inverse prediction module530.

The arithmetic decoder 505 receives the compressed bitstream from theG-PCC compressor 303, and performs arithmetic decoding on the receivedcompressed bitstream to obtain the occupancy symbols and the quantizedprediction residuals. The arithmetic decoding may include any amongvarious entropy decoding algorithms such as, e.g., context-adaptivebinary arithmetic decoding.

The octree decoder 510 receives the obtained occupancy symbols from thearithmetic decoder 505, and decodes the received occupancy symbols intothe quantized positions, using an octree decoding algorithm.

The inverse quantizer 515 receives the quantized positions from theoctree decoder 510, and inverse quantizes the received quantizedpositions, using, e.g., a scaling algorithm and/or a shifting algorithm,to obtain reconstructed positions of the points in the input pointcloud.

The LoD generator 520 receives the quantized positions from the octreedecoder 510, and obtains the LoD of each of the points corresponding tothe received quantized positions.

The inverse quantizer 525 receives the obtained quantized predictionresiduals, and inverse quantizes the received quantized predictionresiduals, using, e.g., a scaling algorithm and/or a shifting algorithm,to obtain reconstructed prediction residuals.

The inverse prediction module 530 receives the obtained reconstructedprediction residuals from the inverse quantizer 525, and receives theobtained LoD of each of the points from the LoD generator 520. Theinverse prediction module 530 obtains reconstructed attributesrespectively of the received reconstructed prediction residuals byapplying a prediction algorithm to the received reconstructed predictionresiduals in an order based on the received LoD of each of the points.The prediction algorithm may include any among various predictionalgorithms such as, e.g., interpolation, weighted average calculation, anearest neighbor algorithm and RDO. The reconstructed attributes are ofthe points in the input point cloud.

The method and the apparatus for interframe point cloud attribute codingwill now be described in detail. Such a method and an apparatus may beimplemented in the G-PCC compressor 303 described above, namely, theprediction module 430. The method and the apparatus may also beimplemented in the G-PCC decompressor 310, namely, the inverseprediction module 530.

The embedded coefficient coding iterates over a set of definedquantization-levels for coarse granular scalable decoding of eachlifting transform coefficient. Given each quantization level, thecorresponding quantization step-size is calculated and the quantizationindices of each coefficient is coded. At each of subsequent quantizationlevels, the same process is repeated on the residual coefficientsresulting from repeatedly subtracting reconstructed layers with theprevious quantization levels. In harmonization with the current GPCCanchor design of coefficient coding, if the aforementioned quantizationindices from the consecutive coefficients are a series of zeros, thezero_cnt is sent instead of explicitly coding those indices. When any ofthe quantization indices happens to have a non-zero value at thatparticular level, zero_cnt is set to 0 and the aforementioned indicesare coded explicitly one by one.

In the following description, a C-like pseudo-code is presented for thedecoding process where reflectance is a representative type ofsingle-channel point-cloud signal.

-   -   quantWeight is the quantization weighting factor described in        Section 2 that the decoder has already available as input    -   reflectance is the output of the decoding process and        initialized to zero at the beginning    -   QPset[N] is the array containing the target QP levels of coarse        granularity scalability where N is the desired number of QP        levels.    -   decodeZeroCnt( ) returns the number of zero coefficients at a        particular quantization level of targeted coarse-grain        scalability for consecutive lifting coefficients being decoded.    -   decodeDelta( ) returns the quantized coefficient value    -   InverseQuantization( ) performs inverse quantization at each        level QP given the quantized coefficient value delta    -   TotalNumLOD, predictorCount[ ], predStartIdx[ ] refer to the        total number of LOD's in the current GPCC design, the number of        points at each LOD, and the predictorIndex of the first point in        the LOD, respectively.

      zero_cnt = decodeZeroCnt( ) for (level = N −1; level >= 0;--level) {  level_qp = QPset[level]   for (LODIndex = 0; LODIndex <TotalNumLOD ; ++LODIndex)   for (cnt = 0; cnt <predictorCount[LODIndex]; ++cnt)   {    predictorIndex = cnt +predStartIdx[LODIndex]    delta = 0    if (zero_cnt > 0) {    zero_cnt--    } else {     delta = decodeDelta( )     zero_cnt =decodeZeroCnt( )    }    reflectance +=InverseQuantization(level_qp,delta)    if (level == 0)     reflectance/= quantWeight[predictorIndex]    } // predictorIndex loop    zero_cnt =decodeZeroCnt( )  } // level loop

In the above description, the double for-loop with LODIndex and cnt wasused to come up with the variable predictorindex which is essentiallyreferring to each point cloud. This can be simply replaced as follows asin the current GPCC anchor implementation. For example,

for (LODIndex = 0; LODIndex < TotalNumLOD ; ++LODIndex)  for (cnt = 0;cnt < predictorCount[LODIndex]; ++cnt)  {   predictorIndex = cnt +predStartIdx[LODIndex] may be replaced with for (predictorIndex = 0;predictorIndex < pointCount;++predictorIndex) {where pointCount is the total number of point cloud in the frame orslice being coded. A potential benefit of the former embodiment is thatone can leverage on any available information available from previousLOD's in order to better code the coefficient values in the current LOD.

It may be appreciated that the same, substantially the same, or similarprocesses as in the single-channel signal can be applied for eachchannel individually for multi-channel cases. Alternatively, one can usethe same, substantially the same, or similar processes by replacing‘reflectance’ with ‘color’ the latter being a vector signal, where thedefinition of ‘zero’ corresponds to the case where all three componentsof the coefficient are zero.

In the following description, a C-like pseudo-code is presented for thedecoding process where color is a representative type of three-channelpoint-cloud signal.

-   -   quantWeight is the quantization weighting factor described in        Section 2 that the decoder has already available as input    -   color is a vector of three dimensions (e.g., RGB or YUV) and the        output of the decoding process and initialized to zero at the        beginning    -   QPset[N] is the array containing the target QP levels of coarse        granularity scalability where N is the desired number of QP        levels.    -   decodeZeroCnt( ) returns the number of zero coefficients at a        particular quantization level of targeted coarse-grain        scalability for consecutive lifting coefficients being decoded,        where the definition of ‘zero’ corresponds to the case where all        three components of the coefficient are zero.    -   decodeDelta( ) returns the quantized coefficient vector    -   InverseQuantization( ) performs inverse quantization at each        level QP given the quantized coefficient vector delta    -   TotalNumLOD, predictorCount[ ], predStartIdx[ ] refer to the        total number of LOD's in the current GPCC design, the number of        points at each LOD, and the predictorIndex of the first point in        the LOD, respectively.

       zero_cnt = decodeZeroCnt( ) for (level = N −1; level >= 0;--level) {   level_qp = QPset[level]    for (LODIndex = 0; LODIndex <TotalNumLOD ; ++LODIndex)    for (cnt = 0; cnt <predictorCount[LODIndex]; ++cnt)    {     predictorIndex = cnt +predStartIdx[LODIndex]     delta = {0,0,0}     if (zero_cnt > 0) {     zero_cnt--     } else {      delta = decodeDelta( )      zero_cnt =decodeZeroCnt( )     }     color += InverseQuantization(level_qp,delta)    if (level == 0)      color /= quantWeight[predictorIndex]     } //predictorIndex loop     zero_cnt = decodeZeroCnt( )   } // level loop

Different context-models for entropy coding can be used in order tobetter leverage different characteristics of coefficients. In oneembodiment, different context-models can be used for different LOD(Level-Of-Details) layers of lifting coefficients as higher LOD layerstend to have smaller coefficients as a result of lifting decomposition.In another embodiment, different context-models can be used fordifferent QP (Quantization Parameter)'s as higher QP's tend to result insmaller quantized coefficients and vice versa. In another embodiment,different context-models can be used for different layers of coarsegranular scalability as enhancement layers (i.e., layers added to refinethe reconstructed signal to a smaller QP level) tend to be of noisier orrandom nature in terms of correlation among coefficients. In anotherembodiment, different context-models can be used depending upon thevalues of or a function of values of the reconstructed (hence availablefor reference) samples from corresponding locations in the lowerquantization-level layers. For example. it is likely that areas withzero or very small reconstructed values in the lower layers havedifferent coefficient characteristics from areas with the oppositetendency. In another embodiment, different context-models can be useddepending upon the values of or a function of values of thereconstructed (hence available for reference) samples from correspondinglocations in the lower LOD's at the same quantization-level. Thesesamples from corresponding locations can be available as a result of thenearest-neighborhood search in LOD building in GPCC. Note these samplesare available at the decoder as well as a result of LOD-by-LODreconstruction as shown in the above pseudo-code. In all of the aboveembodiments, instead of using different context models, one canadaptively switch the look-up-tables for symbol-index coding in the casewhere dictionary-based coding or other methods relying on look-up-tablesare used.

FIG. 6 is a flowchart illustrating a method 600 of interframe pointcloud attribute coding, according to embodiments. In someimplementations, one or more process blocks of FIG. 6 may be performedby the G-PCC decompressor 310. In some implementations, one or moreprocess blocks of FIG. 6 may be performed by another device or a groupof devices separate from or including the G-PCC decompressor 310, suchas the G-PCC compressor 303.

Referring to FIG. 6 , in a first block 610, the method 600 includesreceiving data associated with a point cloud.

In a second block 620, the method 600 includes transforming the receiveddata through a lifting decomposition based on enabling a scalable codingof attributes associated with the lifting decomposition.

In a third block 630, the method 600 includes reconstructing the pointcloud based on the transformed data.

Although FIG. 6 shows example blocks of the method 600, in someimplementations, the method 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6 . Additionally, or alternatively, two or more of theblocks of the method 600 may be performed in parallel.

Further, the proposed methods may be implemented by processing circuitry(e.g., one or more processors or one or more integrated circuits). In anexample, the one or more processors execute a program that is stored ina non-transitory computer-readable medium to perform one or more of theproposed methods.

FIG. 7 is a diagram of a computer system 700 suitable for implementingembodiments.

Computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code including instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by computer central processing units (CPUs),Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 7 for the computer system 700 are examplesin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementing theembodiments. Neither should the configuration of the components beinterpreted as having any dependency or requirement relating to any oneor combination of the components illustrated in the embodiments of thecomputer system 700.

The computer system 700 may include certain human interface inputdevices. Such a human interface input device may be responsive to inputby one or more human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as: gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): a keyboard 801, a mouse 802, a trackpad 803, atouchscreen 810, a joystick 805, a microphone 806, a scanner 807, and acamera 808.

The computer system 700 may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouchscreen 810 or the joystick 805, but there can also be tactilefeedback devices that do not serve as input devices), audio outputdevices (such as: speakers 809, headphones (not depicted)), visualoutput devices (such as screens 810 to include cathode ray tube (CRT)screens, liquid-crystal display (LCD) screens, plasma screens, organiclight-emitting diode (OLED) screens, each with or without touchscreeninput capability, each with or without tactile feedback capability—someof which may be capable to output two dimensional visual output or morethan three dimensional output through means such as stereographicoutput; virtual-reality glasses (not depicted), holographic displays andsmoke tanks (not depicted)), and printers (not depicted). A graphicsadapter 850 generates and outputs images to the touchscreen 810.

The computer system 700 can also include human accessible storagedevices and their associated media such as optical media including aCD/DVD ROM/RW drive 820 with CD/DVD or the like media 821, a thumb drive822, a removable hard drive or solid state drive 823, legacy magneticmedia such as tape and floppy disc (not depicted), specializedROM/ASIC/PLD based devices such as security dongles (not depicted), andthe like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

The computer system 700 can also include interface(s) to one or morecommunication networks 855. The communication networks 855 can forexample be wireless, wireline, optical. The networks 855 can further belocal, wide-area, metropolitan, vehicular and industrial, real-time,delay-tolerant, and so on. Examples of the networks 855 include localarea networks such as Ethernet, wireless LANs, cellular networks toinclude global systems for mobile communications (GSM), third generation(3G), fourth generation (4G), fifth generation (5G), Long-Term Evolution(LTE), and the like, TV wireline or wireless wide area digital networksto include cable TV, satellite TV, and terrestrial broadcast TV,vehicular and industrial to include CANBus, and so forth. The networks855 commonly require external network interface adapters that attachedto certain general purpose data ports or peripheral buses 849 (such as,for example universal serial bus (USB) ports of the computer system 700;others are commonly integrated into the core of the computer system 700by attachment to a system bus as described below, for example, a networkinterface 854 including an Ethernet interface into a PC computer systemand/or a cellular network interface into a smartphone computer system.Using any of these networks 855, the computer system 700 can communicatewith other entities. Such communication can be uni-directional, receiveonly (for example, broadcast TV), uni-directional send-only (for exampleCANbus to certain CANbus devices), or bi-directional, for example toother computer systems using local or wide area digital networks.Certain protocols and protocol stacks can be used on each of thosenetworks 855 and network interfaces 854 as described above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces 854 can be attached to a core 840 of thecomputer system 700.

The core 840 can include one or more Central Processing Units (CPU) 841,Graphics Processing Units (GPU) 842, specialized programmable processingunits in the form of Field Programmable Gate Areas (FPGA) 843, hardwareaccelerators 844 for certain tasks, and so forth. These devices, alongwith read-only memory (ROM) 845, random-access memory (RAM) 846,internal mass storage 847 such as internal non-user accessible harddrives, solid-state drives (SSDs), and the like, may be connectedthrough a system bus 848. In some computer systems, the system bus 848can be accessible in the form of one or more physical plugs to enableextensions by additional CPUs, GPU, and the like. The peripheral devicescan be attached either directly to the core's system bus 848, or throughthe peripheral buses 849. Architectures for a peripheral bus includeperipheral component interconnect (PCI), USB, and the like.

The CPUs 841, GPUs 842, FPGAs 843, and hardware accelerators 844 canexecute certain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in theROM 845 or RAM 846. Transitional data can also be stored in the RAM 846,whereas permanent data can be stored for example, in the internal massstorage 847. Fast storage and retrieve to any of the memory devices canbe enabled through the use of cache memory, that can be closelyassociated with the CPU 841, GPU 842, internal mass storage 847, ROM845, RAM 846, and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of embodiments, or they can be of the kind well known andavailable to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system 700having architecture, and specifically the core 840 can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core 840 that are of non-transitorynature, such as the core-internal mass storage 847 or ROM 845. Thesoftware implementing various embodiments can be stored in such devicesand executed by the core 840. A computer-readable medium can include oneor more memory devices or chips, according to particular needs. Thesoftware can cause the core 840 and specifically the processors therein(including CPU, GPU, FPGA, and the like) to execute particular processesor particular parts of particular processes described herein, includingdefining data structures stored in the RAM 846 and modifying such datastructures according to the processes defined by the software. Inaddition or as an alternative, the computer system can providefunctionality as a result of logic hardwired or otherwise embodied in acircuit (for example: the hardware accelerator 844), which can operatein place of or together with software to execute particular processes orparticular parts of particular processes described herein. Reference tosoftware can encompass logic, and vice versa, where appropriate.Reference to a computer-readable media can encompass a circuit (such asan integrated circuit (IC)) storing software for execution, a circuitembodying logic for execution, or both, where appropriate. Embodimentsencompass any suitable combination of hardware and software.

While this disclosure has described several embodiments, there arealterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods that, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

What is claimed is:
 1. A method of point cloud attribute coding, themethod being performed by at least one processor, and the methodcomprising: receiving data associated with a point cloud; transformingthe received data through a lifting decomposition based on enabling ascalable coding of attributes associated with the lifting decompositionby iterations, over a set of defined quantization-levels for each oflifting transform coefficients, in which a process is repeated onresidual coefficients resulting from repeatedly subtractingreconstructed layers with previous quantization levels; andreconstructing the point cloud based on the transformed data.
 2. Themethod of claim 1, wherein smaller lifting coefficients are generated bythe lifting decomposition for higher level-of-detail layers associatedwith the received data based on one or more context models being usedfor different level-of-detail layers.
 3. The method of claim 1, whereinsmaller quantized coefficients are generated by the liftingdecomposition for higher quantization parameters based on one or morecontext models being used for different quantization parameters.
 4. Themethod of claim 1, wherein one or more context models are used fordifferent layers of coarse granular scalability for the liftingdecomposition based on minimizing noise between coefficients of thelifting decomposition.
 5. The method of claim 1, wherein one or morecontext models are used based on values or a function of values ofreconstructed samples from corresponding locations in lower quantizationlevel layers associated with the received data.
 6. The method of claim1, wherein one or more context models are used based on values or afunction of values of reconstructed samples from corresponding locationsin lower level-of-detail layers having the same quantization level. 7.The method of claim 1, wherein the lifting decomposition is appliedbased on adaptively switching look-up-tables for symbol-index codingbased on dictionary-based coding being used.
 8. A computer system forpoint cloud attribute coding, comprising: at least one memory configuredto store computer program code; and at least one processor configured toaccess the at least one memory and operate according to the computerprogram code, the computer program code comprising: receiving codeconfigured to cause the at least one processor to receive dataassociated with a point cloud; transforming code configured to cause theat least one processor to transform the received data through a liftingdecomposition based on enabling a scalable coding of attributesassociated with the lifting decomposition by iterations, over a set ofdefined quantization-levels for each of lifting transform coefficients,in which a process is repeated on residual coefficients resulting fromrepeatedly subtracting reconstructed layers with previous quantizationlevels; and reconstructing code configured to cause the at least oneprocessor to reconstruct the point cloud based on the transformed data.9. The computer system of claim 8, wherein smaller lifting coefficientsare generated by the lifting decomposition for higher level-of-detaillayers associated with the received data based on one or more contextmodels being used for different level-of-detail layers.
 10. The computersystem of claim 8, wherein smaller quantized coefficients are generatedby the lifting decomposition for higher quantization parameters based onone or more context models being used for different quantizationparameters.
 11. The computer system of claim 8, wherein one or morecontext models are used for different layers of coarse granularscalability for the lifting decomposition based on minimizing noisebetween coefficients of the lifting decomposition.
 12. The computersystem of claim 8, wherein one or more context models are used based onvalues or a function of values of reconstructed samples fromcorresponding locations in lower quantization level layers associatedwith the received data.
 13. The computer system of claim 8, wherein oneor more context models are used based on values or a function of valuesof reconstructed samples from corresponding locations in lowerlevel-of-detail layers having the same quantization level.
 14. Thecomputer system of claim 8, wherein the lifting decomposition is appliedbased on adaptively switching look-up-tables for symbol-index codingbased on dictionary-based coding being used.
 15. A non-transitorycomputer-readable storage medium storing instructions that cause atleast one processor to: receive data associated with a point cloud;transform the received data through a lifting decomposition based onenabling a scalable coding of attributes associated with the liftingdecomposition by iterations, over a set of defined quantization-levelsfor each of lifting transform coefficients, in which a process isrepeated on residual coefficients resulting from repeatedly subtractingreconstructed layers with previous quantization levels; and reconstructthe point cloud based on the transformed data.
 16. The computer readablemedium of claim 15, wherein smaller lifting coefficients are generatedby the lifting decomposition for higher level-of-detail layersassociated with the received data based on one or more context modelsbeing used for different level-of-detail layers.
 17. The computerreadable medium of claim 15, wherein smaller quantized coefficients aregenerated by the lifting decomposition for higher quantizationparameters based on one or more context models being used for differentquantization parameters.
 18. The computer readable medium of claim 15,wherein one or more context models are used for different layers ofcoarse granular scalability for the lifting decomposition based onminimizing noise between coefficients of the lifting decomposition. 19.The computer readable medium of claim 15, wherein one or more contextmodels are used based on values or a function of values of reconstructedsamples from corresponding locations in lower quantization level layersassociated with the received data.
 20. The computer readable medium ofclaim 15, wherein one or more context models are used based on values ora function of values of reconstructed samples from correspondinglocations in lower level-of-detail layers having the same quantizationlevel.